mirror of
https://github.com/bspeice/speice.io
synced 2024-12-22 08:38:09 -05:00
154 lines
6.2 KiB
Plaintext
154 lines
6.2 KiB
Plaintext
---
|
|
slug: 2016/01/cloudy-in-seattle
|
|
title: Cloudy in Seattle
|
|
date: 2016-01-23 12:00:00
|
|
authors: [bspeice]
|
|
tags: []
|
|
---
|
|
|
|
Building on prior analysis, is Seattle's reputation as a depressing city actually well-earned?
|
|
|
|
<!-- truncate -->
|
|
|
|
```python
|
|
import pickle
|
|
import pandas as pd
|
|
import numpy as np
|
|
from bokeh.plotting import output_notebook, figure, show
|
|
from bokeh.palettes import RdBu4 as Palette
|
|
from datetime import datetime
|
|
import warnings
|
|
|
|
output_notebook()
|
|
```
|
|
|
|
```
|
|
BokehJS successfully loaded.
|
|
```
|
|
|
|
## Examining other cities
|
|
|
|
After taking some time to explore how the weather in North Carolina stacked up over the past years, I was interested in doing the same analysis for other cities. Growing up with family from Binghamton, NY I was always told it was very cloudy there. And Seattle has a nasty reputation for being very depressing and cloudy. All said, the cities I want to examine are:
|
|
- Binghamton, NY
|
|
- Cary, NC
|
|
- Seattle, WA
|
|
- New York City, NY
|
|
|
|
I'd be interested to try this analysis worldwide at some point - comparing London and Seattle might be an interesting analysis. For now though, we'll stick with trying out the US data.
|
|
|
|
There will be plenty of charts. I want to know: **How has average cloud cover and precipitation chance changed over the years for each city mentioned?** This will hopefully tell us whether Seattle has actually earned its reputation for being a depressing city.
|
|
|
|
|
|
```python
|
|
city_forecasts = pickle.load(open('city_forecasts.p', 'rb'))
|
|
forecasts_df = pd.DataFrame.from_dict(city_forecasts)
|
|
```
|
|
|
|
|
|
```python
|
|
cities = ['binghamton', 'cary', 'nyc', 'seattle']
|
|
city_colors = {cities[i]: Palette[i] for i in range(0, 4)}
|
|
|
|
def safe_cover(frame):
|
|
if frame and 'cloudCover' in frame:
|
|
return frame['cloudCover']
|
|
else:
|
|
return np.NaN
|
|
|
|
def monthly_avg_cloudcover(city, year, month):
|
|
dates = pd.DatetimeIndex(start=datetime(year, month, 1, 12),
|
|
end=datetime(year, month + 1, 1, 12),
|
|
freq='D', closed='left')
|
|
cloud_cover_vals = list(map(lambda x: safe_cover(forecasts_df[city][x]['currently']), dates))
|
|
cloud_cover_samples = len(list(filter(lambda x: x is not np.NaN, cloud_cover_vals)))
|
|
# Ignore an issue with nanmean having all NaN values. We'll discuss the data issues below.
|
|
with warnings.catch_warnings():
|
|
warnings.simplefilter('ignore')
|
|
return np.nanmean(cloud_cover_vals), cloud_cover_samples
|
|
```
|
|
|
|
|
|
```python
|
|
years = range(1990, 2016)
|
|
def city_avg_cc(city, month):
|
|
return [monthly_avg_cloudcover(city, y, month) for y in years]
|
|
|
|
months = [
|
|
('July', 7),
|
|
('August', 8),
|
|
('September', 9),
|
|
('October', 10),
|
|
('November', 11)
|
|
]
|
|
|
|
for month, month_id in months:
|
|
month_averages = {city: city_avg_cc(city, month_id) for city in cities}
|
|
f = figure(title="{} Average Cloud Cover".format(month),
|
|
x_axis_label='Year',
|
|
y_axis_label='Cloud Cover Percentage')
|
|
for city in cities:
|
|
f.line(years, [x[0] for x in month_averages[city]],
|
|
legend=city, color=city_colors[city])
|
|
show(f)
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Well, as it so happens it looks like there are some data issues. July's data is a bit sporadic, and 2013 seems to be missing from most months as well. I think really only two things can really be confirmed here:
|
|
- Seattle, specifically for the months of October and November, is in fact significantly more cloudy on average than are other cities
|
|
- All cities surveyed have seen average cloud cover decline over the months studied. There are data issues, but the trend seems clear.
|
|
|
|
Let's now move from cloud cover data to looking at average rainfall chance.
|
|
|
|
|
|
```python
|
|
def safe_precip(frame):
|
|
if frame and 'precipProbability' in frame:
|
|
return frame['precipProbability']
|
|
else:
|
|
return np.NaN
|
|
|
|
def monthly_avg_precip(city, year, month):
|
|
dates = pd.DatetimeIndex(start=datetime(year, month, 1, 12),
|
|
end=datetime(year, month + 1, 1, 12),
|
|
freq='D', closed='left')
|
|
precip_vals = list(map(lambda x: safe_precip(forecasts_df[city][x]['currently']), dates))
|
|
precip_samples = len(list(filter(lambda x: x is not np.NaN, precip_vals)))
|
|
# Ignore an issue with nanmean having all NaN values. We'll discuss the data issues below.
|
|
with warnings.catch_warnings():
|
|
warnings.simplefilter('ignore')
|
|
return np.nanmean(precip_vals), precip_samples
|
|
|
|
def city_avg_precip(city, month):
|
|
return [monthly_avg_precip(city, y, month) for y in years]
|
|
|
|
for month, month_id in months:
|
|
month_averages = {city: city_avg_cc(city, month_id) for city in cities}
|
|
f = figure(title="{} Average Precipitation Chance".format(month),
|
|
x_axis_label='Year',
|
|
y_axis_label='Precipitation Chance Percentage')
|
|
for city in cities:
|
|
f.line(years, [x[0] for x in month_averages[city]],
|
|
legend=city, color=city_colors[city])
|
|
show(f)
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
The same data issue caveats apply here: 2013 seems to be missing some data, and July has some issues as well. However, this seems to confirm the trends we saw with cloud cover:
|
|
- Seattle, specifically for the months of August, October, and November has had a consistently higher chance of rain than other cities surveyed.
|
|
- Average precipitation chance, just like average cloud cover, has been trending down over time.
|
|
|
|
## Conclusion
|
|
|
|
I have to admit I was a bit surprised after doing this analysis. Seattle showed a higher average cloud cover and average precipitation chance than did the other cities surveyed. Maybe Seattle is actually an objectively more depressing city to live in.
|
|
|
|
Well that's all for weather data at the moment. It's been a great experiment, but I think this is about as far as I'll be able to get with weather data without some domain knowledge. Talk again soon! |